Description
The students arrive with very different backgrounds. To fill in the gaps in their mathematical, statistical and programming skills, the first 5 weeks of the first year (Master 1) are devoted to a complete refresher program. An additional novel feature of this very intensive educational approach is to place students in a situation where they rapidly work together and help each other in an independent way (without the instructor). They also learn (and hopefully appreciate) to work in an open space. This is very important for achieving one of our goals: by the end of the Master's program, participants with very different backgrounds are used to interacting with each other and intensively working together. During these 5 weeks, they will have different instructors and focus on different topics: Linear and bilinear algebra, complex numbers, Fourier analysis, basic probability, basic statistics and programming in R, differential equations, PDE, simulation, modeling, simulation and implementation of basic systems and object-oriented programming, basics in experimental protocol set-ups.
| Subjects | teacher | face to face hours | ECTS |
|---|---|---|---|
| ECUE: Probability and Statistics | Patricia Reynaud-Bouret | 42 | 3 |
| ECUE: Programming for Modeling and Simulation* | Alexandre Muzy | 48 | 3 |
| ECUE: Linear Algebra | Martin Krupa | 30 | 3 |
| ECUE: Dynamical Systems | Martin Krupa | 30 | 3 |
| ECUE: Introduction to Cognitive Science and Neuroscience | Tobias Scheer, Emilie Gerbier, Ingrid Bethus, Raphaël Fargier | 36 | 3 |
* Description: Nowadays, understanding complex systems, like the neurocognitive one, requires the use of computer programs for modeling and simulating their behaviors. As a computational neuroscientist you need to be able to understand the basics of hardware and software architectures of computers and to develop, organize and connect programming objects. This bootcamp will provide you a method to carry out your programming projects in the context of modeling and simulation. You will learn how to describe programs as algorithms and how to design these programs as interacting objects. Both algorithms and object-oriented designs will be implemented in Python programming language. All along the bootcamp, you will apply the concepts learned to a research project of neuronal network model.